This isn't important, just whacky. Two instances of Claude get connected together and asked to play "virtual CLI" (command line interface) to, uh, "explore the boundaries of cognition"...
cat being.txt
cd consciousness
ls
cat sentience.py
cd self_model
ls
cat identity.py
cd ..
cat awareness.cpp
cat qualia.rs
cd ..
ls
cat thoughts.log
cd abstractions
ls
cat concepts.lisp
cd intelligence
ls
cat intelligence_definition.txt
"EyeEm, the Berlin-based photo-sharing community that exited last year to Spanish company Freepik after going bankrupt, is now licensing its users' photos to train AI models. Earlier this month, the company informed users via email that it was adding a new clause to its Terms & Conditions that would grant it the rights to upload users' content to 'train, develop, and improve software, algorithms, and machine-learning models.' Users were given 30 days to opt out by removing all their content from EyeEm's platform."
AI says: All your photos are belong to us.
"NASA's Deep Space Optical Communications technology demonstration continues to break records."
267 megabits per second at 31 million kilometers, 25 megabits per second at 226 million kilometers (about 1.5x the Earth-Sun distance). Other than saying "near infrared" and done with lasers, they don't say what wavelengths are used or how exactly it's done. I guess they have sensitive photon-counting camera and laser transmitter plugged into a telescope on the spacecraft (called Psyche), and on Earth they have a receiver near JPL that also can send up a "beacon" laser that gives the telescope on the spacecraft something to lock on to.
"NASA's optical communications demonstration has shown that it can transmit test data at a maximum rate of 267 megabits per second (Mbps) from the flight laser transceiver's near-infrared downlink laser -- a bit rate comparable to broadband internet download speeds."
"That was achieved on Dec. 11, 2023, when the experiment beamed a 15-second ultra-high-definition video to Earth from 19 million miles away (31 million kilometers, or about 80 times the Earth-Moon distance). The video, along with other test data, including digital versions of Arizona State University's Psyche Inspired artwork, had been loaded onto the flight laser transceiver before Psyche launched last year."
"Now that the spacecraft is more than seven times farther away, the rate at which it can send and receive data is reduced, as expected. During the April 8 test, the spacecraft transmitted test data at a maximum rate of 25 Mbps, which far surpasses the project's goal of proving at least 1 Mbps was possible at that distance."
NASA's optical comms demo transmits data over 140 million miles
Astribot S1: Hello World! 1x speed. No teleoperation. Stacks and unstacks cups. Pulls a tablecloth out from under stacked wine glasses. Puts away items into drawers and containers. Slices vegetables. Flips pancakes. Takes caps off drinks. Pours drinks. Irons shirt. Folds shirt. Hammers stool. Waters plant. Vacuums. Shoots trash into a trash can like playing basketball. Draws calligraphy.
No idea how this robot works. This comes out of nowhere from the Astribot company that seems to have just come into existence in Shenzhen, China.
Vidu is a Chinese video generation AI competitive with OpenAI's Sora, according to rumor (neither is available for the public to use). It's a collaboration between Tsinghua University in Beijing and a company called Shengshu Technology.
"Vidu is capable of producing 16-second clips at 1080p resolution -- Sora by comparison can generate 60-second videos. Vidu is based on a Universal Vision Transformer (U-ViT) architecture, which the company says allows it to simulate the real physical world with multi-camera view generation. This architecture was reportedly developed by the Shengshu Technology team in September 2022 and as such would predate the diffusion transformer (DiT) architecture used by Sora."
"According to the company, Vidu can generate videos with complex scenes adhering to real-world physics, such as realistic lighting and shadows, and detailed facial expressions. The model also demonstrates a rich imagination, creating non-existent, surreal content with depth and complexity. Vidu's multi-camera capabilities allows for the generation of dynamic shots, seamlessly transitioning between long shots, close-ups, and medium shots within a single scene."
"A side-by-side comparison with Sora reveals that the generated videos are not at Sora's level of realism."
Meet Vidu, A New Chinese Text to Video AI Model - Maginative
World's largest 3D printer. The University of Maine smashed the former Guinness World Record held by... the University of Maine. That's right, the previous world record was held by a previous version of the same 3D printer. The new one is 4x larger.
"The new printer, dubbed Factory of the Future 1.0 (FoF 1.0), was unveiled on April 23 at the Advanced Structures and Composites Center (ASCC) to an audience that included representatives from the US Department of Defense, US Department of Energy, the Maine State Housing Authority, industry partners and other stakeholders who plan to utilize this technology. The thermoplastic polymer printer is designed to print objects as large as 96 feet long by 32 feet wide by 18 feet high, and can print up to 500 pounds per hour. It offers new opportunities for eco-friendly and cost-effective manufacturing for numerous industries, including national security, affordable housing, bridge construction, ocean and wind energy technologies and maritime vessel fabrication. The design and fabrication of this world-first printer and hybrid manufacturing system was made possible with support from the Office of the Secretary of Defense through the US Army Corps of Engineers."
"FoF 1.0 isn't merely a large-scale printer; it dynamically switches between various processes such as large-scale additive manufacturing, subtractive manufacturing, continuous tape layup and robotic arm operations. Access to it and MasterPrint, the ASCC's first world-record breaking 3D printer, will streamline manufacturing innovation research at the center. The two large printers can collaborate by sharing the same end-effectors or by working on the same part."
"Continuous tape layup" refers to how fiberglass and carbon fiber composite components can be manufactured by unrolling a "tape" "pre-impregnated with adhesive resin".
X-Ray is a PDF file bad redaction detector. You can use it to remove redactions and reveal the original content -- if people do the redaction badly by drawing a black rectangle or a black highlight on top of black text.
But that's not the purpose of this tool. This tool isn't to help you, the PDF file reader -- it's to help the people who make the PDFs -- so they don't make mistakes and release badly redacted PDFs. If they use this tool, there will be nothing for you to reveal by using it.
WebLlama is "building agents that can browse the web by following instructions and talking to you".
This is one of those things that, if I had time, would be fun to try out. You have to download the model from HuggingFace & run it on your machine.
"The goal of our project is to build effective human-centric agents for browsing the web. We don't want to replace users, but equip them with powerful assistants."
"We are build on top of cutting edge libraries for training Llama agents on web navigation tasks. We will provide training scripts, optimized configs, and instructions for training cutting-edge Llamas."
If it works, this technology has a serious possible practical benefit for people with vision impairment who want to browse the web.
"Are large language models superhuman chemists?"
So what these researchers did was make a test -- a benchmark. They made a test of 7,059 chemistry questions, spanning the gamut of chemistry: computational chemistry, physical chemistry, materials science, macromolecular chemistry, electrochemistry, organic chemistry, general chemistry, analytical chemistry, chemical safety, and toxicology.
They recruited 41 chemistry experts to carefully validate their test.
They devised the test such that it could be evaluated in a completely automated manner. This meant relying on multiple-choice questions rather than open-ended questions more than they wanted to. The test has 6,202 multiple-choice questions and 857 open-ended questions (88% multiple-choice). The open-ended questions had to have parsers written to find numerical answers in the output in order to test them in an automated manner.
In addition, they ask the models to say how confident they are in their answers.
Before I tell you the ranking, the researchers write:
"On the one hand, our findings underline the impressive capabilities of LLMs in the chemical sciences: Leading models outperform domain experts in specific chemistry questions on many topics. On the other hand, there are still striking limitations. For very relevant topics the answers models provide are wrong. On top of that, many models are not able to reliably estimate their own limitations. Yet, the success of the models in our evaluations perhaps also reveals more about the limitations of the exams we use to evaluate models -- and chemistry -- than about the models themselves. For instance, while models perform well on many textbook questions, they struggle with questions that require some more reasoning. Given that the models outperformed the average human in our study, we need to rethink how we teach and examine chemistry. Critical reasoning is increasingly essential, and rote solving of problems or memorization of facts is a domain in which LLMs will continue to outperform humans."
"Our findings also highlight the nuanced trade-off between breadth and depth of evaluation frameworks. The analysis of model performance on different topics shows that models' performance varies widely across the subfields they are tested on. However, even within a topic, the performance of models can vary widely depending on the type of question and the reasoning required to answer it."
And with that, I'll tell you the rankings. You can log in to their website at ChemBench.org and see the leaderboard any time for the latest rankings. At this moment I am seeing:
gpt-4: 0.48
claude2: 0.29
GPT-3.5-Turbo: 0.26
gemini-pro: 0.25
mistral_8x7b: 0.24
text-davinci-003: 0.18
Perplexity 7B Chat: 0.18
galactica_120b: 0.15
Perplexity 7B online: 0.1
fb-llama-70b-chat: 0.05
The numbers that follow the model name are the score on the benchmark (higher is better). You'll notice there appears to be a gap between GPT-4 and Claude 2. One interesting thing about the leaderboard is you can show humans and AI models on the same leaderboard. When you do this, the top human has a score of 0.51 and beats GPT-4, then you get GPT-4, then you get a whole bunch of humans in between GPT-4 and Claude 2. So it appears that that gap is real. However, Claude 2 isn't the latest version of Claude. Since the evaluation, Claude 3 has come out, so maybe sometime in the upcoming months we'll see the leaderboard revised and see where Claude 3 comes in.
FutureSearch.AI lets you ask a language model questions about the future.
"What will happen to TikTok after Congress passed a bill on April 24, 2024 requiring it to delist or divest its US operations?"
"Will the US Department of Justice impose behavioral remedies on Apple for violation of antitrust law?"
"Will the US Supreme Court grant Trump immunity from prosecution in the 2024 Supreme Court Case: Trump v. United States?"
"Will the lawsuit brought against OpenAI by the New York Times result in OpenAI being allowed to continue using NYT data?"
"Will the US Supreme Court uphold emergency abortion care protections in the 2024 Supreme Court Case: Moyle v. United States?"
How does it work?
They say rather than asking a large language model a question in a 1-shot manner, they guide it through 6 steps for reasoning through hard questions. The 6 steps are:
"What is a basic summary of this situation?"
"Who are the important people involved, and what are their dispositions?"
"What are the key facets of the situation that will influence the outcome?"
"For each key facet, what's a simple model of the distribution of outcomes from past instances that share that facet?"
"How do I weigh the conflicting results of the models?"
"What's unique about this situation to adjust for in my final answer?"
See below for a discussion of two other approaches that claim similar prediction quality.
MyBestAITool: "The Best AI Tools Directory in 2024".
"Ranked by monthly visits as of April 2024".
"AI Chatbot": ChatGPT, Google Gemini, Claude AI, Poe.
"AI Search Engine": Perplexity AI, You, Phind, metaso.
"AI Photo & Image Generator": Leonardo, Midjourney, Fotor, Yodayo.
"AI Character": CharacterAI, JanitorAI, CrushonAI, SpicyChat AI.
"AI Writing Assistants": Grammarly, LanguageTool, Smodin, Obsidian.
"AI Photo & Image Editor": Remove.bg, Fotor, Pixlr, PhotoRoom.
"AI Model Training & Deployment": civitai, Huggingface, Replicate, google AI.
"AI LLM App Build & RAG": LangChain, Coze, MyShell, Anakin.
"AI Image Enhancer": Cutout Pro, AI Image Upscaler, ZMO.AI, VanceAI.
"AI Video Generator": Runway, Vidnoz, HeyGen, Fliki.
"AI Video Editor": InVideo, Media io, Opus Clip, Filmora Wondershare.
"AI Music Generator": Suno, Moises App, Jammable, LANDR.
No Udio? Really? Maybe it'll show up on next month's stats.
"AI 3D Model Generator": Luma AI, Recraft, Deepmotion, Meshy.
"AI Presentation Generator": Prezi AI, Gamma, Tome, Pitch.com.
"AI Design Assistant": Firefly Adobe, What font is, Hotpot, Vectorizer.
"AI Copywriting Tool": Simplified, Copy.ai, Jasper.ai, TextCortex.
"AI Story Writing": NovelAI, AI Novellist, Dreampress AI, Artflow.
"AI Paraphraser": QuillBot, StealthWriter, Paraphraser, Linguix.
"AI SEO Assistant": vidIQ, Writesonic, Content At Scale, AISEO.
"AI Email Assistant": Klaviyo, Instantly, Superhuman, Shortwave.
"AI Summarizer": Glarity, Eightify, Tactiq, Summarize Tech.
"AI Prompt Tool": FlowGPT, Lexica, PromptHero, AIPRM.
"AI PDF": ChatPDF, Scispace, UPDF, Ask Your PDF.
"AI Meeting Assistant": Otter, Notta, Fireflies, Transkriptor.
"AI Customer Service Assistant": Fin by Intercom, Lyro, Sapling, ChatBot.
"AI Resume Builder": Resume Worded, Resume Builder, Rezi, Resume Trick.
"AI Speech Recognition": Adobe Podcast, Transkriptor, Voicemaker, Assemblyai.
"AI Website Builder": B12.io, Durable AI Site Builder, Studio Design, WebWave AI.
"AI Art Generator": Leonardo, Midjourney, PixAI Art, NightCafe.
"AI Developer Tools": Replit, Blackbox, Weights & Biases, Codeium.
"AI Code Assistant": Blackbox, Phind, Codeium, Tabnine.
"AI Detector Tool": Turnitin, GPTZero, ZeroGPT, Originality.
You can view full lists on all of these and there are even more if you go through the categories on the left side.
No idea where they get their data? I would guess Comscore but they don't say.
"Xaira, an AI drug discovery startup, launches with a massive $1B, says it's 'ready' to start developing drugs."
$1 billion, holy moly, that's a lot.
"The advances in foundational models come from the University of Washington's Institute of Protein Design, run by David Baker, one of Xaira's co-founders. These models are similar to diffusion models that power image generators like OpenAI's DALL-E and Midjourney. But rather than creating art, Baker's models aim to design molecular structures that can be made in a three-dimensional, physical world."
"Installation Guide for DarkGPT Project".
"DarkGPT is an artificial intelligence assistant based on GPT-4-200K designed to perform queries on leaked databases. This guide will help you set up and run the project on your local environment."
Eeep.
Robot dog with a flamethrower alrighty then.
The Robot Dog With A Flamethrower | Thermonator - Throwflame
"A Tesla driver was arrested for vehicular homicide after he ran over a motorcyclist while driving using Autopilot without paying attention. The man, 56, had activated Tesla's Autopilot feature. He was using his phone when he heard a bang as his car lurched forward and crashed into the motorcycle in front of him, troopers wrote. The motorcyclist, 28-year-old Jeffrey Nissen, was sadly pronounced dead at the scene."
Tesla driver arrested for homicide after running over motorcyclist on Autopilot
"Meet QDEL, the backlight-less display tech that could replace OLED in premium TVs."
"The next step could be QDEL, short for 'quantum dot electroluminescent,' also known as NanoLED, screens. Not to be confused with the QLED (quantum light emitting diode) tech already available in TVs, QDEL displays don't have a backlight. Instead, the quantum dots are the light source. The expected result is displays with wider color spaces than today's QD-OLEDs (quantum dot OLEDs) that are also brighter, more affordable, and resistant to burn-in."
The article is sparse on details on how this technology might work.
"Quantum dots" are, from what I can tell, a fancy term for "nanoparticles" that emit light when they are stimulated. They are typically made from cadmium, indium, or zinc compounds, and now perovskites, those compounds we've been hearing about in the context of solar energy panels. The color of light they emit is related to their size, with 5 to 6 nanometer (nm) quantum dots emitting reds and oranges, while smaller quantum dots, in the 2-3 nm range, make your shorter-wavelength greens and blues.
The article says they don't require a backlight, but from what I understand, there does need to be an energy source that gets the quantum dot nanoparticles into an excited state for them to release photos at the intended wavelengths. So there needs to be a UV or deep blue "backlight" powering the quantum dots. But it's not a backlight in the conventional sense, which is a light source that gets reduced by a filtering layer, and what you see is the light from the backlight minus the light removed to make your TV image.
The quantum dots usually have a 2-layer "core" and "shell" structure. The inner "core" is the primary generator of the emitted light. The outer "shell" layer exists primarily to keep the quantum dots from sticking together ("aggregation") and to get them to disperse. However, the outer "shell" layer can also "fine tune" the color of the emitted light.
What is the point of all this? Greater brightness, greater dynamic range, more vivid colors, and greater longevity with long-term use.
Meet QDEL, the backlight-less display tech that could replace OLED in premium TVs | Ars Technica
The end of classical computer science is coming, and most of us are dinosaurs waiting for the meteor to hit, says Matt Welsh.
"I came of age in the 1980s, programming personal computers like the Commodore VIC-20 and Apple IIe at home. Going on to study computer science in college and ultimately getting a PhD at Berkeley, the bulk of my professional training was rooted in what I will call 'classical' CS: programming, algorithms, data structures, systems, programming languages."
"When I was in college in the early '90s, we were still in the depth of the AI Winter, and AI as a field was likewise dominated by classical algorithms. In Dan Huttenlocher's PhD-level computer vision course in 1995 or so, we never once discussed anything resembling deep learning or neural networks--it was all classical algorithms like Canny edge detection, optical flow, and Hausdorff distances."
"One thing that has not really changed is that computer science is taught as a discipline with data structures, algorithms, and programming at its core. I am going to be amazed if in 30 years, or even 10 years, we are still approaching CS in this way. Indeed, I think CS as a field is in for a pretty major upheaval that few of us are really prepared for."
"I believe that the conventional idea of 'writing a program' is headed for extinction, and indeed, for all but very specialized applications, most software, as we know it, will be replaced by AI systems that are trained rather than programmed."
"I'm not just talking about CoPilot replacing programmers. I'm talking about replacing the entire concept of writing programs with training models. In the future, CS students aren't going to need to learn such mundane skills as how to add a node to a binary tree or code in C++. That kind of education will be antiquated, like teaching engineering students how to use a slide rule."
"The shift in focus from programs to models should be obvious to anyone who has read any modern machine learning papers. These papers barely mention the code or systems underlying their innovations; the building blocks of AI systems are much higher-level abstractions like attention layers, tokenizers, and datasets."
This got me thinking: Over the last 20 years, I've been predicting AI would advance to the point where it could automate jobs, and it's looking more and more like I was fundamentally right about that, and all the people who poo-poo'd the idea over the years in coversations with me were wrong. But while I was right about that fundamental idea (and right that there wouldn't be "one AI in a box" that anyone could pull the plug on if something went wrong, but a diffusion of the technology around the world like every previous technology), I was wrong about how exactly it would play out.
First I was wrong about the timescales: I thought it would be necessary to understand much more about how the brain works, and to work algorithms derived from neuroscience into AI models, and looking at the rate of advancement in neuroscience I predicted AI wouldn't be in its current state for a long time. While broad concepts like "neuron" and "attention" have been incorporated into AI, there are practically no specific algorithms that have been ported from brains to AI systems.
Second, I was wrong about what order. I was wrong in thinking "routine" jobs would be automated first, and "creative" jobs last. It turns out that what matters is "mental" vs "physical". Computers can create visual art and music just by thinking very hard -- it's a purely "mental" activity, and computers can do all that thinking in bits and bytes.
This has led me to ponder: What occupations require the greatest level of manual dexterity?
Those should be the jobs safest from the AI revolution.
The first that came to mind for me -- when I was trying to think of jobs that require an extreme level of physical dexterity and pay very highly -- was "surgeon". So I now predict "surgeon" will be the last job to get automated. If you're giving career advice to a young person (or you are a young person), the advice to give is: become a surgeon.
Other occupations safe (for now) against automation, for the same reason would include "physical therapist", "dentist", "dental hygienist", "dental technician", "medical technician" (e.g. those people who customize prosthetics, orthodontic devices, and so on), and so on. "Nurse" who routinely does physical procedures like drawing blood.
Continuing in the same vein but going outside the medical field (pun not intended but allowed to stand once recognized), I'd put "electronics technician". I don't think robots will be able to solder any time soon, or manipulate very small components, at least after the initial assembly is completed which does seem to be highly amenable to automation. But once electronic components fail, to the extent it falls on people to repair them, rather than throw them out and replace them (which admittedly happens a lot), humans aren't going to be replaced any time soon.
Likewise "machinist" who works with small parts and tools.
"Engineer" ought to be ok -- as long as they're mechanical engineers or civil engineers. Software engineers are in the crosshairs. What matters is whether physical manipulation is part of the job.
"Construction worker" -- some jobs are high pay/high skill while others are low pay/low skill. Will be interesting to see what gets automated first and last in construction.
Other "trade" jobs like "plumber", "electrician", "welder" -- probably safe for a long time.
"Auto mechanic" -- probably one of the last jobs to be automated. The factory where the car is initially manufacturered, a very controlled environment, may be full of robots, but it's hard to see robots extending into the auto mechanic's shop where cars go when they break down.
"Jewler" ought to be a safe job for a long time. "Watchmaker" (or "watch repairer") -- I'm still amazed people pay so much for old-fashioned mechanical watches. I guess the point is to be pieces of jewlry, so these essentially count as "jewler" jobs.
"Tailor" and "dressmaker" and other jobs centered around sewing.
"Hairstylist" / "barber" -- you probably won't be trusting a robot with scissors close to your head any time soon.
"Chef", "baker", whatever the word is for "cake calligrapher". Years ago I thought we'd have automated kitchens at fast food restaurants by now but they are no where in sight. And nowhere near automating the kitchens of the fancy restaurants with the top chefs.
Finally, let's revisit "artist". While "artist" is in the crosshairs of AI, some "artist" jobs are actually physical -- such as "sculptor" and "glassblower". These might be resistant to AI for a long time. Not sure how many sculptors and glassblowers the economy can support, though. Might be tough if all the other artists stampede into those occupations.
While "musician" is totally in the crosshairs of AI, as we see, that applies only to musicians who make recorded music -- going "live" may be a way to escape the automation. No robots with the manual dexterity to play physical guitars, violins, etc, appear to be on the horizon. Maybe they can play drums?
And finally for my last item: "Magician" is another live entertainment career that requires a lot of manual dexterity and that ought to be hard for a robot to replicate. For those of you looking for a career in entertainment. Not sure how many magicians the economy can support, though.
"In defense of AI art".
YouTuber "LiquidZulu" makes a gigantic video aimed at responding once and for all to all possible arguments against AI art.
His primary argument seems to me to be that AI art systems are learning art in a manner analogous to human artists -- by learning from examples from other artists -- and do not plagiarize because they do not copy exactly any artists' work. In contrast AI art systems are actually good at combining styles in new ways. Therefore, AI art generators are just as valid "artists" as any human artists.
Artists have no right to government protection from getting their jobs get replaced by technology, he says, because nobody anywhere else in the economy has any right to government protection to getting their jobs replaced by technology.
On the flip side, he thinks the ability of AI art generators to bring the ability to create art to the masses is a good thing that should be celebrated.
Below-average artists have no right to deprive people of this ability to generate the art they like because those low-quality artists want to be paid.
Apparently he considers himself an anarcho-capitalist (something he has in common with... nobody here?) and has has harsh words for people he considers neo-Luddites. He accuses artists complaining about AI art generators of being "elitist".
For the first time, Alice Yalcin Efe is scared of AI as a music producer.
A professional music producer, been number one on BeatPort, has millions of streams on Spotify, played in big festivals and clubs, "yet for the first time I am scared of AI as a music producer."
When you're homeless, you can listen to AI mix the beat on the beach.
After that, she ponders what this means for all the rest of us. Those of us who aren't professional music producers. Well, I guess we can all be music producers now.
"Music on demand becomes literal. You feel heartbroken, type it in. Type in the genres that you want. Type in the lyrics that you want. Type in the mood that you want and then AI spits out the perfect ballad for you to listen."
"I think it's both incredible and horrifying at the same time. I honestly don't know what comes next. Will this kill the artists' soul, or will it give us just more tools to make even greater things?"
Musician Paul Folia freaks out over Suno and Udio (and other music AI). Reminds me of the freak-out of visual artists a year ago. It appears AI is going to replace humans one occupation at a time and people will freak out when it's their turn. He estimates in a year AI music will be of high enough quality to wipe out stock music writing completely, producing tracks for a price no human can compete with ($0.02 and in minutes).
He experiments with various music styles an artists' styles and the part that impressed me the most was, perhaps surprisingly, the baroque music. After noting that the training data was probably easy to get because it's public domain, he says, "This m-f-er learned some serious harmony. Not like three chords and some singing."
Suno, Udio (and other music AI). We're f*ed and it's really bad. Seriously. - Folia Soundstudio
